The amount of data available to information seekers has grown astronomically, whether as the result of the proliferation of information sources on the Internet, or as a result of private efforts to organize business information within a company, or any of a variety of other causes. As the amount of available data grows, so does the need to be able to categorize or label that data so that the data may be more efficiently searched. A related problem is the need to rank data that has been identified as responsive to a search query. One approach is to use machine learning systems for this task.
Machine learning systems include such systems as neural networks and machines using kernel-based learning methods, among others. These systems can be used for a variety of data processing or analysis tasks, including, but not limited to, optical pattern and object recognition, control and feedback systems, and text categorization. Other potential uses for machine learning systems include any application that can benefit from data classification.
When a user performs a search for information, that user typically does not simply want a listing of results that simply have some relation to the search query entered by that user but rather wants to be able to quickly access what that user considers to be the best results from within the listing. This is where ranking is important because high rankings indicate to the user that there is a high probability that the information for which the user searched is present in the highest ranked search results.